Method and system for providing a clarity grade for a gem

ABSTRACT

A method and system for generating a clarity grading look-up table includes collecting actual inclusion parameter data for a plurality of gems, where the actual inclusion parameter data includes an actual clarity grade and an actual inclusion parameter data combination. A mathematical relationship between a clarity grade and a particular inclusion parameter combination is then extrapolated from the actual inclusion parameter data. A derived clarity grade is then assigned to a plurality of inclusion parameter combinations as a function of the mathematical relationship and a set of inputted inclusion parameters. Also, a method and system for providing a clarity grade includes receiving a plurality of inclusion characteristics associated with a gem and parameterizing each of the inclusion characteristics, so that a parameter value is assigned to each inclusion characteristic. The parameter values are then input to a mathematical formula so as to provide a parameterized clarity grade for the gem.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 16/396,603 filed on Apr. 26, 2019, which itself isa continuation of and claims priority to U.S. patent application Ser.No. 15/151,902 filed on May 11, 2016 (issued as U.S. Pat. No.10,318,515), which itself is a continuation of U.S. patent applicationSer. No. 13/845,995 filed on Mar. 18, 2013 (issued as U.S. Pat. No.9,366,638), which itself is a continuation of U.S. patent applicationSer. No. 12/287,187 filed on Oct. 7, 2008 (issued as U.S. Pat. No.8,402,066), each of which is hereby incorporated by reference in itsentirety.

TECHNICAL FIELD

The present invention is directed generally towards analyzing a gem, andmore specifically towards parameterizing aspects of the clarity gradingprocess for a gem and forming a clarity grading model and look-up tablefor use in clarity grading of a gem.

BACKGROUND OF THE TECHNOLOGY

All experienced diamond graders understand that clarity grades candiffer because of any number of inclusion characteristics. Suchdifferences may, for example, include differences in an inclusion'ssize, type, position or relief (i.e., brightness). However, gradersgenerally cannot describe exactly how much each characteristic actuallyinfluences the ultimate clarity grade. Instead, graders mostly rely ontheir diamond grading training and experience to provide them with amemory of visual references with which to evaluate each new caseindividually. For example, to ascertain how a SI1 inclusion located nearthe girdle would be graded if it were located in the center of thetable, or how a VS2 inclusion with a Low relief would be graded if ithad a High relief instead, is nearly impossible for a grader to dowithout concrete examples to refer to. In many situations, extensivereliance on concrete examples, however, is impractical since it would bedifficult, expensive, and impracticable to obtain concrete examples ofevery possible inclusion characteristic combination. Consequently, theclarity grading process is vulnerable to the grader subjectivities,which can affect the consistency of clarity grades in the field.

In view of the need for consistency and uniformity in the claritygrading process, developing tools that could more objectively, andpreferably mathematically, predict the influences of particularinclusion characteristics on clarity grade would be extremely helpful.Such tools may then be used to better understand the visual claritygrading decision processes, and to help provide consistency in theseprocesses by providing these tools to grader trainees uniformly.Accordingly, there is currently a need for an improved method and systemfor providing a clarity grade for a gem.

SUMMARY OF THE INVENTION

This invention addresses the aforementioned problems by providing animproved method and system for providing a clarity grade for a gem.

In an embodiment of the present invention, a method is provided forgenerating a look-up table for use in clarity grading a gem. The methodcomprises collecting actual inclusion parameter data and an associatedclarity grade for each of a plurality of gems. From the actual inclusionparameter data and the associated clarity grades, mathematicalrelationships are derived which model interactions between claritygrades and combinations of inclusion parameters. Parameterized claritygrades are then associated with corresponding combinations of inclusionparameter value ranges based upon the derived mathematicalrelationships, so that for a set of input inclusion parameter values, acorresponding parameterized clarity grade is provided.

In another embodiment of the present invention, a method for generatinga clarity grading look-up table is provided which comprises obtainingactual inclusion parameter data for a plurality of gems, wherein theactual inclusion parameter data for each gem includes an actual claritygrade associated with a combination of inclusion parameters representedby the actual inclusion parameter data; deriving from the actualinclusion parameter data mathematical relationships relating to theinfluence of interacting inclusion parameter combinations on claritygrades; and populating a table with a plurality of clarity gradedesignations associated with combinations of ranges of inclusionparameter values as defined by the mathematical relationships.

In a further embodiment of the present invention, a method is providedfor determining a clarity grade for a gem. The method includes receivinga plurality of inclusion characteristics associated with a gem. Each ofthe plurality of inclusion characteristics are parameterized to havecorresponding inclusion parameter values. The corresponding inclusionparameter values are evaluated in accordance with a mathematicalrelationship which models the relative influence of inclusion parametervalues upon clarity grade, and which is selected as a function of theinclusion parameter values. Provided as the clarity grade for the gem isa parameterized clarity grade based upon the evaluation of the inclusionparameter values in accordance with the selected mathematicalrelationship.

In another embodiment of the invention, a computer-readable mediumhaving computer-executable instructions thereon for rendering digitalcontent on a device is provided. Within such embodiment, thecomputer-readable medium includes a first, second, and third module. Thefirst module provides instructions for receiving actual inclusionparameter data for a plurality of gems. The actual inclusion parameterdata for each gem includes an actual clarity grade and an actualinclusion parameter data combination. The second module providesinstructions for deriving a mathematical relationship between a claritygrade and a particular inclusion parameter combination. For thisembodiment, the mathematical relationship is derived from the actualinclusion parameter data. The third module provides instructions forassigning a derived clarity grade to each of a plurality of inclusionparameter combinations, such that the derived clarity grade is afunction of the mathematical relationship and a set of inputtedinclusion parameters.

In a further embodiment of the invention, another method for generatinga clarity grading look-up table is provided. Within such embodiment, themethod includes the step of obtaining actual inclusion parameter datafor a plurality of gems. The actual inclusion parameter data for eachgem includes an actual clarity grade and an actual inclusion parameterdata combination. The method also includes the step of deriving amathematical relationship between a clarity grade and a particularinclusion parameter combination. For this embodiment, the mathematicalrelationship is derived from the actual inclusion parameter data. Themethod also includes the step of populating a table with a plurality ofderived clarity grades, such that each derived clarity grade is afunction of the mathematical relationship and a particular set ofinputted inclusion parameters.

In yet another embodiment of the invention, another computer-readablemedium having computer-executable instructions thereon for renderingdigital content on a device is provided. Within such embodiment, thecomputer-readable medium includes a first, second, third, and fourthmodule. The first module provides instructions for receiving a pluralityof inclusion characteristics associated with a gem. The second moduleprovides instructions for parameterizing each of the plurality ofinclusion characteristics, so that a parameter value is assigned to eachof the plurality of inclusion characteristics. The third module providesinstructions for inputting the parameter value for each of the pluralityof inclusion characteristics into a mathematical formula. And finally,the fourth module includes instructions for providing a parameterizedclarity grade for the gem, where the parameterized clarity grade is anoutput of the mathematical formula.

As will be appreciated upon consideration of the following detaileddescription of the invention and accompanying drawings, there are manyadvantages and features of the present invention, which in turn lead tomany new and useful applications of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart for providing a clarity grade according to anembodiment of the invention.

FIG. 2A is an exemplary guide for evaluating the dimensions of a roundclarity characteristic.

FIG. 2B is an exemplary guide for evaluating the dimensions of a longclarity characteristic.

FIG. 2C is an exemplary guide for evaluating the dimensions of anelliptical clarity characteristic.

FIG. 3A is an exemplary guide for evaluating the location of aninclusion showing the top view of a gem.

FIG. 3B is an exemplary guide for evaluating the location of aninclusion showing the bottom view of a gem.

FIG. 4 is an exemplary worksheet clarity grid according to an embodimentof the invention.

FIG. 5 is an exemplary table summarizing characteristics and/orparameters of inclusions for a collection of different diamondsassembled in accordance with an embodiment of the invention.

FIG. 6 is an exemplary input form for entering inclusion characteristicsinto a data base according to an embodiment of the invention.

FIG. 7 is an exemplary page showing query results for a particularcombination of inclusion parameters according to an embodiment of theinvention.

FIG. 8A is mapping distribution identifying data gaps for crystalinclusions used in the collecting of inclusion parameter and claritygrade data according to an embodiment of the invention.

FIG. 8B is mapping distribution identifying data gaps for featherinclusions used in the collecting of inclusion parameter and claritygrade data according to an embodiment of the invention.

FIG. 9 is a set of log-normal graphs illustrating the relationshipbetween relative inclusion size and clarity at particular locations on agem which was determined in accordance with an embodiment of theinvention.

FIG. 10A is a graph illustrating the relationship between relief andrelative inclusion size for an SI2 gem having a crystal inclusion in thetable which was determined in accordance with an embodiment of theinvention.

FIG. 10B is a graph illustrating the relationship between relief andrelative inclusion size for an SI2 gem having a feather inclusion in thecrown which was determined in accordance with an embodiment of theinvention.

FIG. 11A is a log-normal graph illustrating the relationship betweensize and clarity which shows the best fit line for RW2 crystals in thetable which was determined in accordance with an embodiment of theinvention.

FIG. 11B is a log-normal graph illustrating the relationship betweensize and clarity which shows the best fit line for RW2 feathers in thecrown which was determined in accordance with an embodiment of theinvention.

FIG. 12 is a simplified diagram illustrating a process for identifyinglook up tables/curves to which inclusion parameters for a gem areapplied in order to obtain a clarity grade for the gem in accordancewith an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

The present invention is directed towards providing a method and systemfor providing a clarity grade for a gem in which inclusion parametersare quantified rather than simply categorized into verbal descriptions(e.g., such as “Very Small” (VS) in size). More specifically, thepresent invention applies a new approach to visual clarity grading byfirst identifying and correlating the influences which determine thevisual clarity grade of a diamond, particularly the range from “veryvery small” (VVS) to “heavily included diamonds,” and then breaking downclarity grades into separate yet interacting inclusion parameters.Moreover, the parameter combinations that influence the clarity gradeare broken down into individual inclusion parameters whose additiveproperties form predictable relationships. As a result, numerical upperand lower limits for each of a plurality of parameter combinations maybe defined in order to translate measured and/or parameterized valuesinto a particular clarity grade.

Although any of several inclusion characteristics may influence theultimate clarity grade of a gem, a few characteristics have beenidentified to be particularly influential. Namely, the size, position,relief, number, and type of a gem's inclusions have been identified.Accordingly, a brief description of each is provided below, along with adiscussion of their respective significance.

The size of an inclusion has the strongest overall impact on the claritygrade and the larger the inclusion, the greater the impact. The size ofan inclusion is preferably represented in the face-up view of a diamond,for example, as a two dimensional object. The length and width of a twodimensional inclusion can be measured directly with a microscopeequipped with a measuring graticule. An equation for an ellipse may thenbe fed these measurements and used to approximate the inclusion area.Although a certain degree of error is associated with thisapproximation, which is higher for irregularly shaped inclusions, with asufficient quantity of data, errors can be smoothed out to producegeneral relationships that can be used to predict the influence of theface-up area of an inclusion on the clarity grade. This ellipticalapproximation of inclusion area has been validated by obtaining similarresults with a digital imaging analysis application using a more precisemethod which digitizes the outline of the inclusion, counts the numberof pixels inside the outline, and then converts the number of pixelsinto an inclusion size area or area relative to the size of the diamond.Inclusion size may also be obtained using the techniques of this digitalimaging analysis application. A more detailed description of thisdigital imaging analysis application is provided in U.S. patentapplication Ser. No. 12/287,186, entitled “An Automated System AndMethod For Clarity Measurements And Clarity Grading,” filed even dateherewith, attorney docket number 353397-165954, and incorporated hereinby reference in its entirety (hereafter, “Clarity MeasurementApplication”).

An important aspect of the inclusion size parameter analysis is theconversion of the area of the inclusion into a ratio of the inclusionarea to the size of the diamond. Although most graders would agree thatsimilarly sized inclusions should not equally impact a 1.0 ct stoneversus a 10.0 ct stone, diamond graders cannot explain or predict, in ahypothetical sense, how the size of the diamond will influence theresults. They must first see an example and visually compare theinclusion size to the size of the diamond in order to confidentlyprovide a clarity grade. In contrast, as will be described in thisapplication, by establishing numerical relationships in accordance withan embodiment of the present invention, one can predict the inclusionsize parameter influence on the clarity grade without visualexamination. The inclusion size parameter can be calculated, asdescribed in the Clarity Measurement Application, by the summing of allthe pixels within the inclusion area that are isolated by a script. Thena calculation can be made to find the inclusion area size relative tothe size of the diamond area (the calculation of which is based on thediameter). Such information, in accordance with an embodiment of theinvention, can then be used with information about other inclusionparameters of the gem to predict clarity grade of the gem.

The positioning of an inclusion can also influence the final claritygrade of a gem since an inclusion's position affects its visibility.Inclusions located just under the table (sometimes referred to as the“heart”), for example, are generally much more visible than similarinclusions located under the bezel facets or near the girdle. Also,although an inclusion might be small and located in an inconspicuousplace, if it is reflected in the pavilion facets, it may look like manyinclusions, not just one. When this happens, it is called a reflector,which generally tends to lower the clarity grade more than similar,non-reflecting inclusions. In practice, a grader may thus view andclassify one of two inclusions differently even if both inclusions areof similar relative sizes depending on their position parameter. Thereare two main explanations for this. First, there is a tendency for aninclusion to be more visible when it is located towards the center ofthe diamond (and thus also closer to the center of an observer'sattention) as opposed to a location closer to the girdle. A secondexplanation is that a more explicit facet distribution and facetreflection pattern toward the edge of most diamonds may tend to hideinclusions, and reduce their visibility, making them less important.

One feature of an embodiment of the present invention is theparameterization of inclusion characteristics, that is a categorizing ofinclusions or other clarity characteristics so that such characteristicscan be described, collected and analyzed in a consistent way. Inconnection with a location mapping operation, position identificationguidelines were developed by which the positions (locations) ofinclusions may be parameterized. The inclusion position parameter may bea pixel-based parameter obtained using a mapping feature of the imagingsoftware, or an operator supplied set of information. In accordance witha preferred parameterization approach, the inclusions are sorted intolocations defined as pavilion, girdle, crown, table-crown, and table.These locations will be described in greater detail hereafter inconnection with FIG. 3A. When the inclusion position parameter ispixel-based, the precise location of the inclusion may, for example, bedetermined by the digital gravity point of the inclusion's pixels.

A gem's relief refers to its visibility and is used in accordance withan embodiment of the invention as a categorical measure of the contrastbetween the inclusion and the surrounding facet distribution andreflection pattern of a diamond. As a general rule, the brighter aninclusion is, the more visible an inclusion appears to be to the graderwho may lower the clarity grade as a result. Most inclusions are whiteor colorless, but some can be black, brown, dark red, or green. The darkinclusions are usually easier to see, so they have a greater impact onthe clarity grade than the colorless inclusions.

To determine an inclusion's relief parameter, techniques described inthe Clarity Measurement Application may again be used, wherein a pixelhistogram of the inclusion may be measured relative to the histogram ofan area proximate to the inclusion. The relief of the inclusion is thendetermined by matching the relationship between the two histograms toone of a set of reference images with known relief factors.Alternatively, the relief of an inclusion may be calculated frompixilated image data by using the ratio of the average pixel valuewithin the inclusion to the average pixel value of an area of the imagewith a constant radius surrounding the inclusion.

Generally, although the number of inclusions has been found to have aminor role in influence clarity grade, a sufficient quantity ofadditional inclusions of similar size or reflections of inclusions cantypically lower the clarity grade by a half a grade. As previouslymentioned, additional inclusions may appear as face-up reflections ofinclusions, or mirror images, which can look like additional inclusionsto an observer and are therefore graded the same as additionalinclusions. Also, depending on the location of an inclusion in adiamond, the distribution of facets can cause the inclusion to appearmultiple times or be reflected, especially when the inclusion ispositioned deep and near the culet of the diamond. Notwithstanding thereason(s) why additional inclusions are viewed, a parameter whichaccounts for the number of inclusions may be included via an automaticcorrection factor for reflections and/or manually to account for thetotal number of inclusions.

The type of a diamond's inclusions also influences its clarity grade.Clarity characteristics, according to their type, may be divided intotwo categories: internal and surface-reaching inclusions. Although eachof these categories may be further subdivided according to particularclarity grading procedures, the more common clarity characteristics fortype are whether the inclusions are crystals or feathers. Large breaksin the stone, or feathers, are potentially hazardous, especially if theyreach the table or extend from the crown through the girdle. If present,feathers typically have a lesser impact on the clarity grade thancrystals. In accordance with an embodiment of the present invention,because of the predominance of crystals and feathers, the type parametermay be defined in terms of crystal and feather inclusions with thesemore common clarity characteristics serving as proxies for some of therarer types of inclusions. The actual type parameter may be enteredmanually by an operator.

Another variable that can have an influence on the final clarity graderesult is the durability. This is almost never applicable to internalclarity characteristics, but surface reaching clarity characteristicscan occasionally pose a degree of risk of further breakage or chippingand lower the final clarity grade call. As such, in accordance with anembodiment of the invention, parameters for High, Medium, or Lowdurability risk factors may be considered. Here, however, the scarcityof High and Medium examples has limited efforts to develop a predictiveinfluence of such a durability parameter on the clarity grade.Nevertheless, one of ordinary skill in the art would appreciate thatincluding such a parameter would still be within the scope and spirit ofthe present invention.

By quantifying inclusion characteristics or parameters, such as thosedescribed above, predictable relationships for particular parametercombinations may thus be used to provide a parameterized clarity grade.An exemplary flow chart of how to provide such a parameterized claritygrade, according to an embodiment of the invention, is provided in FIG.1, using a diamond as an example. For this particular embodiment,although the characteristics of size, position, relief, number, and typeof a gem's inclusions are used, it should be appreciated that thesecharacteristics are used solely for exemplary purposes and that otherembodiments may include any of a plurality of inclusion characteristiccombinations, including characteristics not mentioned here. It shouldalso be noted that the grade setting inclusions were focused upon indeveloping the primary inclusion parameter relationships, while thenumber of inclusions and reflections were considered together as anadditional influence on the clarity grade. Also, as discussedhereinafter, the relief parameter was consolidated from initially fivecategories into only three (i.e., High, Medium and Low), and otherselections were made in specifying how inclusions characteristics may beparameterized in accordance with various embodiments of the invention.

As illustrated in FIG. 1, the process is begun at step 10. Relativeinclusion area operation, step 40, then receives an inclusion areaobtained in step 20 and the area of the diamond obtained in step 30 todetermine a relative inclusion area. A parameterized relief value isobtained in step 50. A parameterized location (or position) value isobtained in step 60. A parameterized type value is obtained at step 70.These values—relative area, location, type, and relief—are then receivedin step 80 where they are used to find an appropriate lookup curve ortable, and from which a numerical clarity grade is determined whichcorresponds to that particular combination of parameter values. Thenumerical clarity grade is then adjusted in step 100 according to thenumber of reflections and/or additional inclusions entered at step 90.This results in a final categorical (or parameterized) clarity gradebeing provided at step 110.

In an alternative embodiment, for steps 90 and 100, the number ofinclusions data field may be replaced by a reflections data field.Possible values for this data field may be none, moderate or obvious,for example. Other values may be used as appropriate. In step 90 forthis alternative embodiment, such values for the reflections observed inthe gemstone would be entered. Then in step 100, for this alternativeembodiment, the numerical grade received from step 80 would be adjustedto account for the entered reflection values. For example, for areflection value of “moderate” the numerical grade would be increased byan amount. For a reflection value of “obvious” the numerical grade wouldbe increased by an even greater amount.

One of ordinary skill will appreciate that, while particular operations,forms or quantities are set forth in the blocks of FIG. 1, other similaror equivalent operations may be used as appropriate to implement theprinciples of the invention. For example, while a “lookup curve” is setforth in step 80, other mechanisms may be used such as look-up tables,data bases, or the like. Likewise, while a calculate operation is setforth in step 80, one of ordinary skill in the art will appreciate thatother operations can provide the desired results, such as by way of lookup tables, or programmed logic arrays, or the like. Similarly, whilespecific quantities, such as 5 and 0.5 are set forth, other values maybe used within the spirit of the invention.

In order to convert parameters such as relative inclusion area, type ofinclusion, relief, and location into a clarity grade, relationshipsbetween these factors and actual clarity grades given in a gradinglaboratory were established. To develop these relationships, datapertaining to thousands of inclusions were measured according to amultifold data collection program. However, because some combinations ofparameters are extremely rare, measuring every known combination ofinclusion parameters so as to fill-out a complete clarity grid isimpractical. Efforts were thus initially focused on the most commonparameter combinations, wherein research was confined to measuringsingle grade-setting inclusions in round brilliant cut diamonds. In thecourse of the study some of the initial data field categories for eachof the inclusion parameters, such as the inclusion type parameter, thenumber of inclusions parameter, and the relief parameter, were combinedto increase the amount of data in each data field category and thusprovide more robust relationships. It is noted that for this study, theface-up position was the main observation direction for visual claritygrading of all clarity grades from VVS2 down. Therefore, in this studythe face-up position was adopted as the standard observation directionfor data collection and for taking digital images. Other observationdirections, however, such as those arrived at by tilting may also beconsidered.

A brief summary of an exemplary data collection methodology forassembling clarity grading data according to an embodiment of theinvention will now be discussed. First, a detailed uniform set of datacollection guidelines and examples were produced for use by a datagathering grading staff, so as to provide uniformity and consistency inthe gathered data. In FIGS. 2A-2C, an exemplary guide for evaluating thedimensions of a clarity characteristic is illustrated. FIG. 2A, forexample, may be used by a grader to determine the “size” and “length”parameters of a “round” or “about round” characteristic. FIG. 2Aprovides dimensions for both a reference round shape 200 and a sampleround inclusion 210. As can be seen in these diagrams, a “round” shapecharacteristic has size and length parameters that are approximately thesame. This is indicated by the same letter, “A”, appearing in both the xand y dimensions. For such a “round” shape, the grader is instructed toenter “A” for its “size,” and “A” for its “length,” where A is themeasured quantity.

Similarly, the guidelines for a “long” characteristic may includereference long shape 220 and sample long inclusion 230 as shown in FIG.2B. A “long” shape characteristic, according to FIG. 2B, has a lengthdimension “B” which is longer (e.g. visibly greater) than the size “A”dimension. Preferably, the grader is instructed to note that theinclusion has a “long” shape characteristic if its length is at leastfour (4) times the size. The measured values for the size “A” and length“B” are entered. From FIG. 2B it can be appreciated that “long” shapecharacteristics include shapes which are rectangle-like as well astriangle-like.

Guidelines for an elliptically-shaped characteristic may also beprovided, which may include reference ellipse shape 240 and sampleelliptical inclusion 250 as shown in FIG. 2C. As can be seen from FIG.2C, an “ellipse-shaped” clarity characteristic has a length “B” which isvisibly greater than its size “A,” and a generally curved or oval shape.The measured values for size “A” and length “B” are entered.

The data collection guidelines may also be drafted so as to provideuniformity and a parameterization for the position parameter. FIGS.3A-3B, for example, may be provided to graders so as to associate aninclusion with one of several possible position parameter designations.In this example, five possible locations are designated: Table 300;Table/Crown 310; Crown 320; Girdle 330; and Pavilion 340. A narrativemay also be provided, which further specifies the boundaries of eachlocation. For example, a position in Table 300 may require theinclusion's center of gravity to be within 80% of the table size. Aposition in Table/Crown 310 may require the inclusion's center ofgravity to be within a region extending from the Table 300 boundary upto a boundary at about 50% of the star facets. A position in Crown 320may require the inclusion's center of gravity to be within a regionextending from the Table/Crown 310 boundary up to a boundary at about ⅓of the upper girdle half. A position in Girdle 330 may require theinclusion's center of gravity to be within Girdle 330. A position inPavilion 340 may require the inclusion's center of gravity to beanywhere in Pavilion 340.

It should be further noted that the data collection guidelines may alsoprovide parameter guidance as to any of several more parameters, aswell. Relief parameters, for example, may be quantified orparameterized. For example, instead of verbal relief assessments “veryhigh relief,” “high relief,” “medium relief,” “low relief,” and “verylow relief,” numerical values 1-5, respectively, may be used, along witha designation of whether the relief has a white or black characteristic,as set forth in Table 1, for example:

TABLE 1 Initial Parameterized Alternate Parameterized Verbal Designation“Relief” Categories “Relief” Categories very high relief RW1 or RB1 RW1or RB1 [High] high relief RW2 or RB2 medium relief RW3 or RB3 RW2 or RB2[Medium] low relief RW4 or RB4 RW3 or RB3 [Low] very low relief RW5 orRB5

It is to be noted that while five relief categories are identifiedabove, a fewer number of categories was ultimately adopted for use.Specifically, the relief parameter was consolidated from initially fivecategories into only three (i.e., High, Medium and Low or RW1, RW2 andRW3), with relief categories 1 and 2 being merged to correspond to a“High” relief category, and relief categories 4 and 5 being merged tocorrespond to a Low relief category. These alternate relief categoriesare noted in the third column of Table 1.

The “number of inclusions” parameter may also be streamlined orparameterized. For example, the numerical value 1 may be assigned ifthere is only one image; a value of 2 may be assigned if there are twoimages; a value of 3 may be assigned if there are 3-4 images; a value of5 may be assigned if there are 5-7 images, and a value of 8 may beassigned if there are 8-10 images.

These foregoing guidelines are then used in a exemplary data collectionmethodology for assembling clarity grading data to gather inclusioncharacteristic data for each diamond in the collection of diamonds inthe data base. A data collection worksheet, such as the worksheetprovided in FIG. 4, may be used for initially recording the data. As canbe seen from FIG. 4, data for up to three inclusions can be accommodatedby the worksheet. The fields entitled Diameter or Weight, Length, Shape,Clarity Grade Diamond (and High, Medium, and Low) refer to the gem as awhole and the classifications assigned by the grading laboratory. Thetable, then provides a number of fields including fields for themagnification used; the inclusion “size” and “length” and whether it is“long”; clarity grade and three possible positions within the assignedclarity grade (High, Medium and Low); whether the inclusion is internalor surface-reaching; “position”; “Number”; “Type”; and “Relief” (Blackor White).

A microscope equipped with a measuring graticule may be used to measurethe length and width of the inclusion for the inclusion size parameter.These measurements are then noted on the worksheet in terms of “microns”or a number of “graticule scales”. The “graticule scale” quantity can beconverted to microns (or other dimensional units) as a function of themicroscope's objective magnification or magnification factor. Forexample, a 1× magnification may result in one graticule scale equaling100 microns; a 2× magnification may result in one graticule scaleequaling 50 microns; 2.5× magnification may result in one graticulescale equaling 40 microns; and a 4× magnification may result in onegraticule scale equaling 25 microns. Here, explicitinstructions/guidance may be provided to the grader via the datacollection guidelines to make note of such information.

For this particular example, it should be appreciated that the datacollection methodology included a preliminary trial part, a trialevaluation part, and then a full program for collecting dailymeasurements and other data in multiple labs. In practice, the datacollection was only done after the final clarity grade assigned by thelab was known for each diamond. The diamond was then examined by one ofthe appointed grading staff who made note of all the relevant claritygrading details and inclusion parameters on the worksheet. A digitalimage of each stone was acquired as a permanent visual reference and forworking with a parallel computerized data processing system provided bythe previously referenced Clarity Measurement Application.

Next, tools described in the Clarity Measurement Application were usedto verify the data on the worksheets by cross checking them with detailsthat can be seen on acquired images. To this end, the graticule scalesmay, for example, be recalculated in accordance with the microscopemagnification factor to convert the inclusion Length and Widthmeasurements into microns.

In the last step, the manually acquired data collection details from theworksheets were entered into a database, which allowed the data to besystematically queried at a later time. Where applicable, theabbreviations from the data collection guidelines were transformed intocommon descriptions used to construct the database.

Once data has been collected, relational database tools can then be usedto ascertain mathematical relationships, by which a clarity grade may bepredicted for particular parameter combinations. For example, a database may be created using the Access Database product by MicrosoftCorporation of Redmond, Wash. The data base consists of three relationaltables (i.e., Table Inclusion 1, Table Inclusion 2 and Table Inclusion3). Preferably, for each diamond studied, information from up to threegrade-setting inclusions can be entered via the database's objectelectronic forms. The three tables (Tables Inclusion 1, 2 and 3) arepreferably designed to contain all the necessary inclusion parametersneeded for the theoretical clarity analysis including the measurementsof each of the diamonds being studied and the details for up to threegrade setting inclusions.

An exemplary Table Inclusion page, according to an embodiment of theinvention, is provided in FIG. 5. For example, in the Table of FIG. 5,the diamond having control number 59 had a weight of 0.7 cts, a diameterof 5.74 mm, and a “Round” shape. A clarity grade of SI2 had beenassigned to the diamond. The inclusion that was examined was judged tohave a “Relief White 3” relief of size 1200 and length 120, and to be“long.” The inclusion was located in the “Table” position and was asingle inclusion. The type of the inclusion was determined to be a“feather.” In the table, the “High”, “Medium” and “Low” fields areselected during the grading process in the laboratory, to indicate wherethe diamond is positioned in the range associated with the assignedclarity grade. Thus, for the example being discussed, diamond controlnumber 59 was judged to be positioned at the high side of the SI2clarity grade range.

In the Access database program, each specific data field in a databaseobject table is specifically defined as numeric, as text, etc., asappropriate for the type of data (measured, verbally described orcalculated) for each data field. The control number is set as theprimary key to link the tables in the database structure. For thisparticular embodiment, a number of queries were designed to sort forinclusions with a type of “crystal” and “feather,” in addition tocalculating the relative inclusion area (inclusion area to diamond area)for each inclusion.

In a preferred embodiment, inclusion characteristics are entered throughinput forms, which are linked to the tables. An example of such an inputform, provided in FIG. 6, allows for easy toggling from one data fieldto another for fast data input. As can be seen from FIG. 6, data entryfields are provided for each of the fields found in the worksheet ofFIG. 4. Drop down menus are provided for parameterized characteristics,such as for position, number, type, and others. For this particularembodiment, there were three input forms for up to three grade settinginclusions linked to the related tables.

An advantage of using a relational database is that subsets of the datacan be formed via queries of existing or additional data fields. Anexample of a queried page, according to an embodiment of the invention,is provided in FIG. 7. For this particular embodiment, an initial querywas made which required added data fields for the metric calculation ofthe inclusion area (in square mm; e.g., based on the equation of anellipse, which requires inclusion “Length” and “Size” measurements), andthe diamond area (in square mm; e.g., based on the diameter of thestone). For subsequent queries, a data field may be added whichcalculates the relative inclusion area (expressed as a percentage) basedon the two previous calculations. For queries on crystals and feathers,still more data fields may be added which link the Tables (i.e., TableInclusion 1, 2 and 3) allowing sorting of the data from the Tables bythe relative inclusion area. In connection with the data collection andvalidation process, databases from different grading laboratories shouldpreferably initially be kept separate until a comparison between thecontents of the databases (e.g. to verify consistent data gathering andevaluation techniques and criteria) indicates that the data can besafely merged into a single common database.

In order to better appreciate particular aspects of the presentinvention, results from an actual test case are now provided. For thiscase, an initial test sample selection of 250 stones was used toevaluate data collection guidelines and to look for preliminaryrelationships between the inclusion parameters and the assigned claritygrade. From this evaluation, predictions were made about the amount ofdata that would be needed to provide statistically sound relationships.

Testing the 250 sample stones was carried out in two gradinglaboratories, which initially included two separate databases that wereupdated as new data became available. In order to ascertain whether thetwo databases could be merged, a comparison between the contents of eachdatabase was made by running a number of similar queries on bothdatabases. This comparison showed no significant difference between thetwo databases when sorting by relative inclusion area and comparing sizeaverages with corresponding clarity grades, with other inclusionparameters held constant. Therefore, because records for the twodatabases were sufficiently similar and compatible, both databases werecombined into a single database. As a result, the amount of data wasincreased, which provided more robust mathematical relationships betweenthe individual parameters and the clarity grade to be calculated.

Mapping the distribution of the data across all the parameters and datafields provided an assessment of where incomplete and missing datafields still existed. The regular mapping allowed for adjusting the datacollection program to fill in the gaps or combinations of parameterswith sparse data. The mapping also provided a prediction of how manyrecords were still needed to provide minimal requirements for eachcombination of parameters to establish statistically soundrelationships. Exemplary mapping distributions are provided in FIGS.8A-8B.

In FIG. 8A, for example, a mapping distribution of inclusions having a“crystal” type across all parameters and data fields is illustrated. Asillustrated, entry 810 indicates that five records exist for a diamondhaving a VS2 clarity grade, with the crystal inclusion located in theTable position; and has a parameterized white relief parameter of 1. Gap820, however, indicates that no records had been entered for a diamondhaving a VS2 clarity grade, with a crystal inclusion located in theTable/Crown position; and a parameterized white relief parameter of 1.

Similarly, FIG. 8B provides a mapping distribution of feathers acrossall parameters and data fields. As illustrated, entry 830 indicates thatone record exists for a diamond having a SI1 clarity grade, wherein thediamond includes a feather inclusion located in the Table position; andhas a parameterized white relief parameter of 1. Gap 840, meanwhile,indicates that no records have been entered for a diamond having a SI2clarity grade, wherein the diamond includes a feather inclusion locatedin the Table position; and has a parameterized white relief parameter of1.

For this particular test case, it should be noted that the dataset wasenhanced by combining the manually collected data with data collectedusing the techniques described in the earlier referenced ClarityMeasurement Application, by the inclusion's area size, position, andrelief can be measured in a semi-automated way. It should be noted thatthe techniques described in the Clarity Measurement Applicationautomatically calculate the inclusion area and relative inclusion areaonce the digital images have been calibrated. To be compatible with theexisting database structure (database objects and data fields), thesearea calculations were converted to inclusion Length and Size parameterdata fields in order to match the existing database. This was possibleby defining a fixed inclusion Length value for each clarity grade to beused in a formula to back calculate the inclusion Size value.

As an example of how this back calculation is done, consider a diamondhaving the following parameter values from an existing data base andbased upon manual inclusion measurements:

Diameter=6.48 mm

Weight=1.02 cts

Shape=RBC

In the original manual inclusion measurements, the width was measured as3 graticule scales at 2× magnification. This corresponds to 150 μm,based upon 50 μm per graticule unit at 2× magnification.

The length was measured as 17 graticule scales at 2× magnification. Thiscorresponds to 850 μm, based upon 50 μm per graticule unit at 2×magnification.

The original determination of inclusion area in microns was based uponthe relationship:

Area inclusion: (([Table inclusion 1].[Size Inclusion]*[Table inclusion1].[Length Inclusion])*(314/400))/1000000

In the above relationship, [Table inclusion 1].[Size Inclusion]corresponds to the value of the “size” field for inclusion 1 of subjectgem in the table for the, and [Table inclusion 1].[Length Inclusion]corresponds to the value of the “length” field for inclusion 1 of thesubject gem in the table.

Recall from the description, provided earlier, of the data collectionmethodology used to assemble the clarity grading data analyzed inconnection the invention, that an equation for an ellipse may often beused to approximate inclusion area using the above “size” and “length”measurements:

Area_(inclusion)≈Area_(ellipse)=π×(size/2)×(length/2)

The Area inclusion relationship above, thus implements an ellipse areacalculation in which, in the numerator π is approximated as 3.14 andmultiplied by 100 to provide an integer form. In the denominator thevalue of 400 is obtained from the divide by 2 factors for the “size” and“length”, and includes a multiply by 100 to account for the integerconversion of the approximation of π. The value of 1,000,000 in thedenominator of the Area inclusion relationship is used to convert squaremicrons into square millimeters (mm).

Applied to the above quantities, the result is:

inclusion area size=0.100 square mm

Then the diameter of the diamond is used to calculate the diameter faceup area of the diamond using an approximation for the area of a circle:3.14×(d/2)², where d=diameter of the diamond. The relative inclusionarea is then calculated using the data base fields of[Query400crystalAREAcal].[Area_inclusion] and[Query400crystalAREAcal].[Area diamond], according to the relationship:

Area relative:([Query400crystalAREAcal].[Area_inclusion].[Query400crystalAREAcal].[Areadiamond])×100

where a multiply by 100 is used to transform the result into percentageform.

Applied to the above quantities, the result is:

relative area inclusion=0.304%

This result is multiplied by 1000 to transform it into integer form foruse in plotting and deriving Relief versus Relative Area relationships.

For the above diamond, the clarity grade assigned by the lab was “SI12”.

As discussed above, as part of the data collection process in order toenhance the dataset used in developing the clarity grading methodologiesof the present invention, diamonds were remeasured using the pixilatedimage analysis techniques to provide inclusion measurements. Thesemeasurements were then converted to inclusion Length and Size parameterdata fields in order to be in a consistent format with the existingdatabase, and so as to make it possible to use the existing data basedata fields. An example of such conversion will be now described:

For the same diamond described above, the inclusion area measured usingpixilated image analysis was:

area inclusion=0.115

From the pixilated image based measured area inclusion, and the diamondarea calculated by the measured diamond diameter, a relative inclusionarea was calculated to be:

relative area inclusion=0.348

The pixilated image based area inclusion was then converted into Sizeand Length values by using a fixed value for the Size, depending on theclarity grade assigned to the diamond having that inclusion. Preferably,the particular magnitudes are chosen in order to avoid overly largevalues for Length. Preferably, the fixed values for Size may be 10 for aVS clarity grade, 100 for a SI clarity grade, and 1000 for a I claritygrade. Using those fixed Size values yields Conversion Values, such as127400, 12740, or 1274 (for VS, SI, or I, clarity grades, respectively)which when multiplied with the area inclusion value, results in areconverted Length value. The Conversion Values can be derived from theformula for calculating Length, which is:

Length(in μm)=(Area_(inclusion)(in square mm from pixilated imageanalysis measurement)×1,000,000×(400/314))/S(fixed,in μm)

Or:

Length(in μm)=Area_(inclusion)(square mm)×1,274,000/S(μm)

Therefore,

Conversion Value=1,274,000/S(μm)

The following table sets forth for each clarity grade, the preferredfixed Size value, and resulting the Conversion Value.

Assigned Clarity Grade Fixed Size value Conversion value VS 10 127400 SI100 12740 I 1000 1274

For the diamond of the example, the clarity grade previously assignedwas SI2. This corresponds to a fixed value for the Size=100, and aConversion Value of 12740. Thus,

Length=0.115×12740=1465 μm.

When this is entered into the data base the relative areainclusion=0.348 (%) which is the same as calculated by the pixilatedimage analysis described herein.

It is noted that the earlier manual measurements made for the diamond ofthis example yielded a relative area inclusion of 304 (%×1000), whilethe pixilated image analysis yielded 348 (%×1000). For this example, asin most examples, the margin of error (304 versus 348) between themanual measurement and the application measurement is considered to bevery acceptable.

It should be further noted that, the efficacy of the data collectionapproach using techniques described in the Clarity MeasurementApplication depends on the quality of the digital images produced.Namely, high resolution digital images are required and must be takenunder lighting conditions suitable for illuminating inclusions withouttoo many reflections hindering the identification of the inclusions. Anapparatus for achieving such high quality images is provided in U.S.patent application Ser. No. 12/287,188, issued as U.S. Pat. No.8,289,621, entitled “Reflected Dark Field Method And Apparatus,” filedeven date herewith, attorney docket number 353397-165953 andincorporated herein by reference in its entirety.

Once sufficient data was collected using techniques described in theClarity Measurement Application, a comparison study between the firstand second data collection methodologies was made to determine thefeasibility of combining both sets of data for future analysis of thecomplete data record set. To assure full compatibility of all the data,the original manually measured records were also digitized using thetechniques described in the Clarity Measurement Application with theresults converted to match the form of second, computerized datacollection records. More specifically, the same diamonds for whichmanually measured records already existed, were processed again usingthe techniques described in the Clarity Measurement Application, and theobtained parameter values were converted to be compatible with the datafields in the original measurement records. As such, this conversioninsured that both data sets could be legitimately merged into a singledatabase for analysis.

Two independent methodologies were developed to analyze the collecteddata in an effort to empirically model the relationships between theinclusion or clarity parameters and the clarity grade. The firstmethodology was a “most reasonable fit” method based on a reasonable fitaverage for a selection of records. This first method makes use ofselecting only the most robust records from the database. For thismethod, records that returned a questionable final result, or hadcorrupt data fields, were excluded from the selection. Moreover, asubjective criteria was used which set boundaries for each parameterbased on a visual assessment of the average and range of values in eachadjacent category. Records were then excluded if they did not fallwithin the set boundaries, which effectively removed outliers fromconsideration in the analysis. Records with overlapping final resultswere also re-examined and excluded from the selection when the overlapcould not be logically justified. For the remaining selection ofrecords, the statistical operators mean, minimum, and maximum of theinclusion size parameter were plotted against each of the otherinclusion parameters.

The second method was a “standard statistical” method, which appliedstatistics that utilized all the records in the database, includingoutliers. The use of the second method, although scientifically moresound than the first, was more vulnerable to the effects of corrupteddata. The second method was particularly vulnerable for parametercombinations having a small sample size, since the average of only a fewrecords is relatively more susceptible to erroneous or misleadingoutliers than the average of a larger set of records. For the “standardstatistical” method, the inclusion size parameter was plotted againsteach of the other inclusion parameters using the same statisticaloperators that were applied in the “most reasonable fit” method, butalso including median. The entire dataset including outliers was used. Acomparison of the results of the two methods was then made to see if amore standard statistical approach (the “standard statistical” method)provided better or worse predictive value than the “most reasonable fit”approach. Although both methods worked adequately, the “most reasonablefit” method was selected as the preferred method.

In support of the analysis of both methods, diamond images werere-examined by clarity graders when related data records differed in aninexplicable manner from the general trend of other observations. Inthese cases, the data entries were also checked again. Although thisselective procedure introduced a degree of bias to the analysis, it wasbelieved that the benefits of addressing obvious mistakes outweighed thenegative consequences of potential bias. It should also be noted that,because both analysis methods indicated that the inclusion parametersfor crystals and feathers influenced the clarity grade differently,crystals and feathers were treated and analyzed separately.

Analytical modeling of the relationships allowed a weight to be given toinclusion parameters for the size, the position and the relief for onegrade setting inclusion. As mentioned previously, retaining as much asfive relief categories was not desirable as the subtle distinctions werenot reproducible and having five categories also left too many gaps inthe clarity grid. Combining relief factors boosted the numbers ofdiamonds in each category significantly enough to fill in most of thegaps.

Despite this effort to consolidate data, however, some clarity grid gapsstill existed since some types of clarity characteristics (e.g., knotsand bruises) are rare and examples are difficult to obtain in sufficientnumbers. Because other types of inclusions such as feathers and crystalsare commonplace, this test case concentrated on those feather andcrystal inclusions to address most of the diamonds in the sample set. Itwas also found that common clarity characteristics can serve as proxiesfor some of the rarer types. For example, a proxy for “knots” may be“crystal;” a proxy for “bruises” may be “crystal;” a proxy for “denseclouds” may be “crystal.”

As a result of the foregoing efforts, for inclusions having a “type” offeathers or crystals, relationships between relative inclusion size andclarity, for each combination of variables, were found to belogarithmic. An exemplary illustration of such a logarithmicrelationship is shown in FIG. 9, wherein log-normal graphsrepresentative for crystals having “medium” relief (RW2) are provided.As illustrated, each of the lines represents one of the parameterizedinclusion locations—Table, Table/Crown, Crown, or Girdle—where thegrade-setting inclusions are located. It can be seen that each line isnearly linear on a logarithmic normal plot, implying a straightforwardexponential relationship. For example, Table Plot 900 represents thisrelationship for inclusions located in the Table region; Table/CrownPlot 910 represents this relationship for inclusions located in theTable/Crown region; Crown Plot 920 represents this relationship forinclusions located in the Crown region; and Girdle Plot 930 representsthis relationship for inclusions located in the Girdle region. Thevertical axis in FIG. 9 represents the “average inclusion sizelogarithmic,” which relates to the logarithm of the relative size of aninclusion, where the plotted relative size values has been multiplied bya factor of 1000 to eliminate the decimal point for convenience increating these plots. The horizontal axis in FIG. 9 is divided up intoseven (7) ranges representing parameterized clarity grades: VS1, VS2,SI1, SI2, I1, I2 and I3. The points plotted in FIG. 9 represent theaverage relative size (logarithmic) for inclusions in diamonds havingthe same parameterized clarity grade, and where the inclusions have a“type” of “crystal,” and a relief of “RW2,” in each of the differentpossible “locations.” For example, in FIG. 9 it can be seen that theaverage size (logarithmic) of inclusions in diamonds assigned a SI1clarity grade, where the inclusions were located in the Girdle region,was approximately 250, while for diamonds with inclusions located in theTable region the average size (logarithmic) of the inclusions wasslightly greater than 100.

Relationships between position, size, and clarity grade, where reliefand type are held constant (here, medium relief and crystal type), canthus be readily ascertained through FIG. 9. For example, FIG. 9 showsthat more relative inclusion area is needed at the girdle position thanthe table position to yield the same clarity grade. As a furtherexample, from FIG. 9 it can be seen that for a diamond having aninclusion with a type of “crystal”, a relief of “RW2”, and a relativesize (logarithmic) of 1000, if that inclusion is positioned in theGirdle region (see Girdle Plot 910), the clarity grade would bepredicted to be “SI2.” However, if that same inclusion were positionedin the Table region (see Table Plot 900), the predicted clarity gradewould be “I1.”

Relationships between other characteristics, may also be ascertained. InFIG. 10A, for example, the relationship between relief and relative area(normal, multiplied by scaling factor of 1000) for an SI2 grade gem isshown, for inclusions having a type of “crystal” and which are locatedin the Table region. As illustrated, more relative inclusion area isneeded for a low relief (R4) inclusion to equal the same SI2 grade as ahigh relief (R2) inclusion.

Regression analysis (either via the “most reasonable fit” method or the“standard statistical” method) was then used to find the “best fittingequation” for plots of various clarity grades corresponding toparticular parameter combinations found in the dataset. Theserelationships were then used as the basis for a model which predicts theclarity grade when the inclusion size and all the other combination ofparameters are known. These predictive models may also be embodied aslook-up tables in a computer application.

An exemplary log-normal plot illustrating such a best-fit equation isprovided in FIG. 11A. For this particular illustration, it should beappreciated that “RW2” refers to the relief (R) category of whitecrystal inclusions (W), and a relief factor of 2 (recalling that theoriginal five relief categories were combined into three categories).The numbers on the x-axis refer to clarity grades where grade 1=VVS2,grade 2=VS1, 3=VS2, 4=SI1, 5=SI2, 6=7=12, and 8=13. The formula shown onthe plot is the equation of the line 1000 that is drawn to best fit theaverage of the observations of white “crystal” inclusions in the Tableregion having relief factor 2 (RW2). For this particular example, thebest-fit equation was determined to be:

y=1.7758e^(1.3271x)

Solving for “x” in the above equation, yields the relationship for aclarity grade value of:

x=(ln(y/1.7758))/1.3271

Another exemplary log-normal plot illustrating such a best-fit equationis provided in FIG. 11B. For this particular illustration, it should beappreciated that “RW2” refers to the relief (R) category of whitecrystal inclusions (W), and a relief factor of 2. The formula shown onthe plot is the equation of the line 1010 that is drawn to best fit theaverage of the observations of white “feather” inclusions in the Crownregion having relief factor 2 (RW2). For this particular example, thebest-fit equation was determined to be:

y=8.0491e^(1.1201x)

Solving for “x” in the above equation, yields the relationship for aclarity grade value of:

x=(ln(y/8.0491))/1.1201

To evaluate performance, results from both the “most reasonable fitmethod” and the “standard statistical method” were compared with actuallab grade results once the application with the look up tables wasbuilt. A selection of records was made for a “test on crystals” and a“test on feathers”. Only examples with solid grades were considered, notborderline cases. In evaluating the results mismatching results wereparticularly evaluated since these would directly contradict thealgorithms that were used to produce the look up tables. Selections ofborderline cases would have alternative explanations that have little todo with the analysis method. A sufficient number of matching results asdetailed here suggests that the look up tables were indeed workingproperly.

In practice, the testing of the look up tables of the applicationproceeded as follows. After loading an image from one of two selectedfile folders (i.e., crystal or feather), an operator ran the differentsteps of the application until the application returned an actuallaboratory clarity grade, as well as a sub-grade for each of theanalysis methods. For testing purposes, operator interaction wasallowed, wherein manual (but usually minor) changes to one of theparameters was occasionally necessary to derive reasonable results. Mostof the time, it was found that these minor changes were associated witheither the relief parameter or the inclusion size parameter, whereaschanges to the position parameter were rare.

For the “test on crystals,” a selection of 241 records had only 10records that did not match the lab grade. In 8 of the 10 records thatdid not match, both analytical methods returned a grade different thanthe lab grade. The “strict agreement rate” percentage for the “test oncrystals” selection was 88% based on the results of the best performingmethod operating on the parameter values as input without modification.A “practical” agreement rate of 96%, however, was achieved when anoperator was permitted to override particular input parameters, such asrelief, that the operator felt were unreasonable.

Meanwhile, for the “test on feathers,” 35 of the 263 selected recordsdid not match the lab grade. In 5 of the 35 records that did not match,a grade similar to that of the lab was returned by one of the methods.For the other 30 records, however, both methods returned a gradedifferent than the lab. When comparing the grades from each of the twomethods to the final grade assigned by the operator, the number ofmatches on a total of 263 selected records was 206 for the first methodand 197 for the second method. Both the “most reasonable fit” and the“standard statistical” methods were found to provide satisfactoryresults, with the “most reasonable fit” method being considered slightlybetter and selected for further use, including use for the “test oncrystals”. The “strict agreement” rate percentage for the selection offeathers was 78% based on the results of the best performing method,whereas the manual override “practical” agreement rate percentage forthis selection was 87%.

In summary, the above evaluation of the present invention'sfunctionality yielded favorable results. The combined theoreticalagreement rate for feathers and crystals was 83%, wherein operatorinteraction improved the agreement rate to 91%. It should also be notedthat no records (either for crystals or feathers) for either analyticalmethod ever differed from the lab grade by more than a grade. Also,except for the VVS2/VS1 clarity grade border for crystals, there was noeasily definable difference between the two analyzing methods. Asmentioned earlier, the “most reasonable fit” method was selected as themethod for further use in the application.

In practice, the present invention thus provides a reasonably accurateclarity grade estimate based on particular gem/inclusion characteristicsidentified by a grader. Namely, a tool is provided in whichgem/inclusion characteristics identified by a grader (both numeric andverbal) are parameterized and provided to a system that outputs aclarity grade based on the particular combination of parameterizedvalues obtained. In order to better appreciate how the present inventionmight be applied, a specific example is provided below of obtaining theclarity grade of a round-cut diamond.

Referring back to FIG. 1, first, the grader may gather informationregarding an inclusion's relative size for use in steps 20, 30 and 40.To this end, it should be appreciated that such information may beobtained in any of several ways, including manually or via thetechniques set forth in the above referenced Clarity MeasurementApplication. Moreover, this information may include a verbal assessmentof the relative size (e.g., “very small”) which is converted to acorresponding numerical value, or an actual measurement taken eithermanually or via the techniques set forth in the Clarity MeasurementApplication. Inclusion relief characteristics are received andparameterized in step 50; position or location values are parameterizedin step 60, and a type parameter is input in step 70.

Each of the parameterized values are then input to a system (e.g., alook-up table or computer program) that outputs an initial numericalgrade estimate G_(initial) as a function of the parameterized values,wherein the following equation may be used:

G_(initial)=f(P_(size),P_(position),P_(type),P_(relief))

Namely, the above equation preferably represents a predeterminedmathematical relationship between preliminary numerical grade estimatesG_(initial) and particular combinations of parameterized valuesP_(size), P_(position), P_(type), and P_(relief).

Next, in steps 90 and 100, a grader may account for additionalinclusions/reflections in the diamond by lowering G_(initial) by apredetermined constant K_(additional), depending upon how influentialadditional inclusions are deemed to be. Where no additionalinclusions/reflections are present, G_(initial) is passed directly tostep 110 for conversion in to a categorical clarity grade from among:IF, VVS1, VVS2, VS1, VS2, SI1, SI2, I1, I2 and I3, for example. Ifadditional inclusions/reflections are present, K_(additional) might beset at 0.5 such that the initial grade estimate G_(initial) is increasedby 0.5 if more than five additional inclusions are detected; a moremoderate number of additional inclusions/reflections may increase thegrade by 0.75; and a more severe number of additionalinclusions/reflections may increase the grade by a full 1.0 grade. Afinal numerical grade G_(final) may then be characterized by thefollowing equation:

G_(final)=G_(initial)+K_(additional)

Once a final numerical grade is determined, the numerical grade isconverted into an actual categorical grade according to a predeterminedrange of values. Namely, the final numerical grade may represent aparameterized clarity grade value that could then be converted to anactual clarity grade. For example, a particular range of values forG_(final) could be assigned to a clarity grade of VS1 (e.g., whereX<G_(final)≤Y), whereas a different range of values might be assigned toa clarity grade of VS2 (e.g., where Y<G_(final)≤Z). Similarly, ranges ofG_(final) would be specified for the other categorical (orparameterized) Clarity Grades: IF, VVS1, VVS2, SI1, SI2, I1, I2 and I3.

As a more specific example, assume that inclusion characteristic valueswere obtained for a diamond such as those listed for control number140912101 in FIG. 7. Such values would indicate a single inclusion witharea of 0.007 mm², a diamond area of 25.415 mm², a position in the Crownregion, a “type” of “crystal”, and pixel values for the inclusion thatwould be parameterized to a relief of Relief White 2 (RW2). The relativesize of the inclusions would be determined to be 0.007/25.415=0.000275or 0.028%. Applying a scaling factor, such as ×1000, used in FIGS. 9-11,a relative size value of 28 would be assigned to the inclusion, in step40 of FIG. 1.

The various inclusion parameter values (P_(size)=28, P_(position)=Crown,P_(relief)=RW2, P_(type)=crystal, number=1) would then be used in steps80 and 100, FIG. 1, to obtain a numerical clarity grade, G_(initial),and then final numerical grade, G_(final). In step 80, a look-up tableor an equation (similar to those illustrated in FIGS. 11A and 11B) forRW2 crystals located in the crown, would be used to determine thecorresponding G_(initial) for P_(size)=28. Then, in step 100, it wouldbe determined if an adjustment was needed in view of the number ofinclusions found in the stone. The final numerical grade G_(final) wouldthen be converted into a categorical clarity grade in step 110.

Referring to FIG. 12, one possible sequence of operations is illustratedfor implementing step 80 of FIG. 1 in accordance with an embodiment ofthe present invention. The operations of step 80 involve identifyinglook up tables/curves to which inclusion parameters for a gem areapplied in order to obtain a numerical clarity grade for the gem.Following the inputting of inclusion parameter values at 1200, step 1210thereafter queries the received inclusion parameter values to identifywhether the type of the inclusion is a crystal or a feather. For theexample diamond being considered, the type is indicated to be “crystal”,therefore step 1220 would next be accessed to identify the relief value.

In step 1220, there are six (6) possible relief values shown for thisexample, three for White Reliefs—RW1, RW2, and RW3—and three for BlackReliefs—RB1, RB2, and RB3. For the diamond being considered, RW2 hasbeen received as relief value, therefore step 1230 would next beaccessed to identify the position value.

In step 1230, depending upon the position value received, the processingwould be routed to a Numerical Grade Versus Size block 1240 for Tableregions, a Numerical Grade Versus Size block 1250 for Table-Crownregions, a Numerical Grade Versus Size block 1260 for Crown regions, ora Numerical Grade Versus Size block 1270 for Girdle regions, for furtherprocessing. Each of these blocks 1240 to 1270 also receives the relativesize value from the input inclusion values at 1200, and each has alook-up table or executes a mathematical model relating relative size toclarity grades. For example, block 1240 can implement a look-up table orexecute a mathematical model corresponding to an equation such as shownin FIG. 11A or 11B, as discussed above, which relates relative size toclarity grades for crystals, in the Table region, with relief values ofRW2. At step 1280, the determined numerical Clarity Grade is output forprocessing by step 100 of FIG. 1. One of ordinary skill in the art wouldunderstand that FIG. 12 illustrates a progression of processing for onecombination of the inclusion parameters, and would understand from FIG.12 how to implement similar progressions of processing for othercombinations of inclusion parameters.

For the diamond being considered, the position value is “Crown”,therefore block 1260 would be accessed, which would implement a look-uptable or execute a mathematical model similar to the equation shown inFIG. 11A, but which relates relative size to clarity grades forcrystals, in the Crown region, with relief values of RW2. Although theplots in FIG. 9 do not set forth the ultimate models/equationsdetermined for the plotted values (such as in FIGS. 11A and 11B), theplots do provide some indication of the Clarity Grade values that may bepredicted by the ultimate models/equations. Referring specifically tothe Crown Plot 920, it can be seen that the plot relates to inclusionsthat are crystals having an RW2 relief value and located in the Crownregion. For the diamond being considered, the relative size value wasdetermined to be 28. Applying this relative size value to Crown Plot920, it can be seen that a Clarity Grade in the VS2 range would bereturned. This is in agreement with the Clarity Grade of VS2 thatappears in FIG. 7 for diamond number 140912101.

It is to be understood that in the preferred embodiment of theinvention, a numerical Clarity Grade would be returned by step 80 ofFIG. 1 (see step 1280 of FIG. 12) and then further processed, asdescribed above, in step 100 to apply adjustments based upon the numberof reflections or additional inclusions in the gem being graded, andconverted in step 110 to a categorical (or parameterized) Clarity Grade.

The present invention has been described above with reference to severaldifferent embodiments. However, those skilled in the art will recognizethat changes and modifications may be made in the above describedembodiments without departing from the scope and spirit of theinvention. Furthermore, while the present invention has been describedin connection with a specific processing flow, those skilled in the artwill recognize that a large amount of variation in configuring theprocessing tasks and in sequencing the processing tasks may be directedto accomplishing substantially the same functions as are describedherein. These and other changes and modifications which are obvious tothose skilled in the art in view of what has been described herein areintended to be included within the scope of the present invention.

What is claimed is:
 1. A method, comprising: at a computer with aprocessor and memory, receiving pixelated image data for a gem, whereinthe image data includes an overall gem image and any inclusion withinthe overall gem image; calculating an area of the gem using the receivedpixelated image data; identifying any inclusion in the receivedpixelated image data for the gem; determining a relative inclusion areausing the area of the gem and the identified inclusions; determining arelief parameter for each identified inclusion in the gem; determining aparameterized location value for each identified inclusion in the gem;determining a type of inclusion for each identified inclusion in thegem; and determining a clarity grade for the gem based on the relativeinclusion area, relief parameter, parameterized location, and type. 2.The method of claim 1 wherein the relief parameter is calculated using acalculated ratio of an average pixel value within each inclusion to anaverage pixel value of an area of the pixelated image with a constantradius surrounding the inclusion.
 3. The method of claim 1 furthercomprising, adjusting the determined clarity grade based on reflectionsidentified in the received pixelated image data for the gem.
 4. Themethod of claim 3 further comprising, further adjusting the adjusteddetermined clarity grade based on additional inclusions identified inthe received pixelated image data for the gem.
 5. The method of claim 1further comprising, calculating a derived clarity grade for the gem as afunction of the relief parameter for each inclusion, and theparameterized locations of the identified inclusions.
 6. The method ofclaim 1 wherein determining a clarity grade includes using the relativeinclusion area, relief parameter, parameterized location, and type asinputs into at least one of a curve, table, and database.
 7. The methodof claim 1 wherein the type is at least one of, internal and surfacereaching inclusions.
 8. The method of claim 1 wherein the type is atleast one of, internal crystal, internal feather, surface reachingcrystal and surface reaching feather.
 9. The method of claim 1 whereinthe parametrized location value is at least one of girdle, crown,table-crown, and table.
 10. A non-transitory computer-readable mediumincluding one or more sequences of instructions that, when executed byone or more processors, cause the processors to perform operationscomprising: receiving pixelated image data for a gem, wherein the imagedata includes an overall gem image and any inclusion within the overallgem image; calculating an area of the gem using the received pixelatedimage data; identifying any inclusion in the received pixelated imagedata for the gem; determining a relative inclusion area using the areaof the gem and the identified inclusions; determining a relief parameterfor each identified inclusion in the gem; determining a parameterizedlocation value for each identified inclusion in the gem; determining atype of inclusion for each identified inclusion in the gem; anddetermining a clarity grade for the gem based on the relative inclusionarea, relief parameter, parameterized location, and type.
 11. Thenon-transitory computer-readable medium of claim 10 wherein the reliefparameter is calculated using a calculated ratio of an average pixelvalue within each inclusion to an average pixel value of an area of thepixelated image with a constant radius surrounding the inclusion. 12.The non-transitory computer-readable medium of claim 11 furthercomprising, adjusting the determined clarity grade based on reflectionsidentified in the received pixelated image data for the gem.
 13. Thenon-transitory computer-readable medium of claim 12 further comprising,further adjusting the adjusted determined clarity grade based onadditional inclusions identified in the received pixelated image datafor the gem.
 14. The non-transitory computer-readable medium of claim 10further comprising, calculating a derived clarity grade for the gem as afunction of the relief parameter for each inclusion, and theparameterized locations of the identified inclusions.
 15. Thenon-transitory computer-readable medium of claim 10 wherein determininga clarity grade includes using the relative inclusion area, reliefparameter, parameterized location, and type as inputs into at least oneof a curve, table, and database.
 16. The non-transitorycomputer-readable medium of claim 10 wherein the type is at least oneof, internal and surface reaching inclusions.
 17. The non-transitorycomputer-readable medium of claim 10 wherein the type is at least oneof, internal crystal, internal feather, surface reaching crystal andsurface reaching feather.
 18. The non-transitory computer-readablemedium of claim 10 wherein the parametrized location value is at leastone of girdle, crown, table-crown, and table.